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The digitalisation of agri-food networks is increasingly presented as a pathway toward more intelligent, efficient, transparent, and resilient food systems. In this narrative, artificial intelligence functions as the analytical brain of the food system, converting dispersed data from farms, supply chains, markets, and consumers into actionable intelligence. This promise has become central to contemporary visions of smart farming, precision logistics, demand forecasting, and waste reduction. Yet the language of food system intelligence often conceals a more troubling political-economic transformation. The infrastructures that collect, process, and monetise agri-food data are frequently controlled by proprietary platforms, agribusiness firms, technology corporations, and data intermediaries. As a result, digitalisation may expand not only analytical capacity but also platform dependency, data concentration, and asymmetrical control over decision-making. This critical review interrogates the tension between food system intelligence and platform dependency in digital agri-food networks. It examines how AI, data infrastructures, and platform governance reshape farmer autonomy, market structure, and public authority. The review draws on critical data studies, political economy, agri-food governance, and platform studies to question the assumption that more data automatically produces more democratic or sustainable food systems. The review concludes that the central policy question is not whether AI should be used in agri-food systems, but who governs it, who benefits from it, and whose knowledge counts within it. Without deliberate governance intervention, digital agri-food networks risk becoming extractive data monopolies rather than intelligent public goods. A more democratic digital food future requires data sovereignty, farmer-centred infrastructures, public-interest governance, and stronger collective control over agri-food data systems.

Smart farming increasingly operates through digital platforms that connect farm machinery, sensors, remote-sensing services, advisory systems, agricultural inputs, data analytics, and decision-support applications. Although these arrangements may improve coordination and service integration, their governance implications cannot be evaluated through technological functionality or productivity expectations alone. This article develops an original conceptual model for examining platform governance in smart farming as a socio-technical problem involving data power, platform dependency, value creation, value capture, farmer autonomy, and accountability. The model begins with platform ecosystem mapping, actor identification, and agricultural data-flow analysis. It then assesses how data access, control, portability, interoperability, asymmetry, algorithmic influence, switching costs, and service bundling may shape dependency relationships. A parallel value pathway distinguishes ecosystem value creation from value capture and distribution among farmers, platform operators, technology providers, advisers, suppliers, complementors, cooperatives, and public institutions. Farmer autonomy is examined through decision rights, informed participation, practical agency, human oversight, trust, digital inclusion, and access to contestation and remedy. The model further links identified risks to governance mechanisms such as data stewardship, transparency, auditability, interoperability requirements, participatory governance, farmer representation, accountability structures, and dispute resolution. Its main contribution is a structured framework for evaluating smart farming platforms through power, dependency, distribution, and agency rather than platform performance alone. The model supports conceptual analysis and research design but requires contextual implementation and empirical validation.